saikat Gochhait
@siu.edu.in
Assistant Professor Academic Level 12 7th Pay CPC
Symbiosis International Deemed University
Dr. Saikat Gochhait teaches at Symbiosis Institute of Digital & Telecom Management, Symbiosis International Deemed University Pune, India and Neurosciences Research Institute-Samara State Medical University, Russia. He is Ph.D and Post-Doctoral Fellow from the UEx, Spain and National Dong Hwa University, Taiwan. He was Awarded DITA and MOFA Fellowship in 2017 and 2018. His research publication with foreign authors is indexed in Scopus, ABDC, and Web of Science. He is a Senior IEEE member.
EDUCATION
Post Doctoral Fellow - Uex, Spain
Post Doctoral Fellow - National Dong Hwa University, Taiwan
PhD - Sambalpur University
RESEARCH INTERESTS
Technology Management
Marketing
Healthcare
Entrepreneurship
FUTURE PROJECTS
Neurosciences
NeuroMarketing
Applications Invited
Collaborators
Entreprenuership
Women Entrepreneurs
Applications Invited
Collaborators
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Saikat Gochhait, Yogesh Singh Rathore, Irina Leonova, Mahima Shanker Pandey, Bal Krishna Saraswat, Santosh Kumar Maurya, Hare Ram Singh, and Nidhi Bansal
Institute of Advanced Engineering and Science
<p>URL stands for uniform resource locator are the addresses of the unique resources on the internet. We all need URLs to access any type of resource on the internet, such as any web page, and document. Sometimes URLs can be long, irrelative and unattractive and unable to send sometimes via email. So, for this, we proposed a URL shortener web application based on the Python-Django platform which is fast and makes your long URLs in the shortest form which you can share on social media platforms. It makes all the messy, unattractive URLs short and shareable. Writing paper proposed a premium section in our application that gives access to the customizable URLs and analytics of your shorten URLs. Customizable URLs are the URLs you create by your own keywords. By creating a premium profile with the application, you can create your own URLs by using your own keywords. We have considered security a major part of the application that prevents the short URLs from being hacked or redirected to any advertising website or content. We store all the data related to the URL to show you the best view of your analytics and update it regularly. Main contribution in this field that for web application that provides users with a fast, secure and shortest URL for their using long URLs. Comparatively to other services that are currently available, the application provides superior security, availability, and confidentiality.</p>
Osama Al-Baik, Saleh Alomari, Omar Alssayed, Saikat Gochhait, Irina Leonova, Uma Dutta, Om Parkash Malik, Zeinab Montazeri, and Mohammad Dehghani
MDPI AG
A new bio-inspired metaheuristic algorithm named the Pufferfish Optimization Algorithm (POA), that imitates the natural behavior of pufferfish in nature, is introduced in this paper. The fundamental inspiration of POA is adapted from the defense mechanism of pufferfish against predators. In this defense mechanism, by filling its elastic stomach with water, the pufferfish becomes a spherical ball with pointed spines, and as a result, the hungry predator escapes from this threat. The POA theory is stated and then mathematically modeled in two phases: (i) exploration based on the simulation of a predator’s attack on a pufferfish and (ii) exploitation based on the simulation of a predator’s escape from spiny spherical pufferfish. The performance of POA is evaluated in handling the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that POA has achieved an effective solution with the appropriate ability in exploration, exploitation, and the balance between them during the search process. The quality of POA in the optimization process is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that POA provides superior performance by achieving better results in most of the benchmark functions in order to solve the CEC 2017 test suite compared to competitor algorithms. Also, the effectiveness of POA to handle optimization tasks in real-world applications is evaluated on twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems. Simulation results show that POA provides effective performance in handling real-world applications by achieving better solutions compared to competitor algorithms.
Ibraheem Abu Falahah, Osama Al-Baik, Saleh Alomari, Gulnara Bektemyssova, Saikat Gochhait, Irina Leonova, Om Parkash Malik, Frank Werner, and Mohammad Dehghani
Tech Science Press
Naga Venkata Yaswanth Lankadasu, Devendra Babu Pesarlanka, Ajay Sharma, Shamneesh Sharma, and Saikat Gochhait
IEEE
Skin cancer is one the most frequent type of cancer in the world. Early detection and diagnosis are vital for effective treatment. Deep learning has been determined to be efficacious in the categorization of skin cancer. In this paper the author has presented a deep learning approach for classifying skin cancer. The algorithm was trained on approximately 10000 photos of skin cancer. In the approach author has used convolutional neural network (CNN) for skin cancer classification. The CNN model is next trained on a collection of skin data tagged as benign or malignant. In the validation of our method is done using a publicly accessible database of skin images. To train dataset, our method obtains ~92% accuracy. For the test set, the model achieves an accuracy of more than 95%. The model can accurately categorize both benign and malignant skin cancer. The predicted model is a useful method for skin cancer early detection and treatment.
Saira Yaqub, Saikat Gochhait, Hafiz Abdul Haseeb Khalid, Syeda Noreen Bukhari, Ayesha Yaqub, and Muhammad Abubakr
IEEE
WhatsApp has become a widely used medium to communicate in the modern era of technology, fostering diverse conversations and expressions among millions of users worldwide. This research introduces a robust analytical tool, the “WhatsApp Chat Ana-lyzer,” crafted to dissect and visualize the multifaceted landscape of group chats on WhatsApp. Imbued with Python's prowess and fortified by Streamlit, matplotlib, and Seaborn, the tool transcends conventional analyses by providing nuanced insights into user behavior, message statistics, and emerging content trends. In this research, we embark on an exploratory journey to decipher the complex dynamics embedded within WhatsApp group chats. By amalgamating sophisticated data preprocessing techniques, advanced statistical analyses, and captivating visualizations, the “WhatsApp Chat Analyzer” stands as a testament to our commitment to unraveling the facts of modern digital communication.
Sakshi, Chetan Sharma, Shamneesh Sharma, Tushar Sharma, and Saikat Gochhait
IEEE
Mustard plants are a crucial agricultural commodity for food and oil production. However, they are frequently vulnerable to various diseases that can substantially reduce crop yield. Early identification and diagnosis of these illnesses are essential for efficient management and control, ensuring sustainable production and agricultural output continuity. This research presents a new method for categorizing mustard leaf diseases using Convolutional Neural Networks (CNNs). The study utilizes sophisticated machine learning algorithms to differentiate between healthy and diseased mustard leaves, which is crucial for maintaining agricultural output and guaranteeing food security. The study involves training CNN architectures—Sequential CNN, ResNet-50, VGG, and AlexNet— on a meticulously curated dataset comprising images of healthy and diseased mustard leaves. The classifier's effectiveness is validated through comprehensive testing, demonstrating significant precision, recall, and F1-score advancements over conventional methods. This approach provides an efficient tool for disease detection in mustard crops and contributes to sustainable agricultural practices, aligning with the global goal of food security and environmental sustainability.
Venkateswara Reddy Lakkireddy, R. Madana Mohana, B. Rama Ganesh, Lakkireddy Udanth Reddy, Saikat Gochhait, and Shrish Chogle
IEEE
Waste Disposal Technology (WDT) selection is a primary issue in Municipal Solid Waste (MSW) that affects the development of the environmental and economic perspectives/aspects, particularly in developing countries. The selection of appropriate WDT is a complex Multi-Attribute Decision-Making (MADM) problem with both qualitative and quantitative elements. The existent MADM approaches with fuzzy sets (removal of uncertainty), different subjective weight methods (significance of attributes), and rank reversal phenomenon leads to improper selection of WDT due to the involvement of different opinions of decision-makers. To avoid this, a Decision Support Framework (DSF) was proposed for optimal WDT selection for the growth of economic and environmental development. The proposed DSF integrates Preference Selection Index (PSI) and a Modified-Comprehensive distance Based Ranking (M-COBRA) approaches to determine the significance of attributes and ranking the alternatives, respectively. The DSF is illustrated using a case study collected from Iran and compared with state-of-the-art MADM approaches. Further, the DSF is validated in terms of sensitivity analysis, rank reversal phenomenon, and Pearson's rank correlation coefficient to ensure the stability of ranking.
Madala Guru Brahmam, Vijay Anand R, Veena Grover, and Saikat Gochhait
IEEE
There is a growing need for firms, organizations, and industries to prioritize requirements, which emphasizes the need for an efficient method to satisfy customers. When combined with the Vertical Binary Search approach, the Majority Voting Goal Based (MVGB) prioritization strategy provides a complete solution for arranging needs in order of importance. In this paper, the MVGB and Vertical Binary Search technique are explained in detail, along with a 4-step methodical approach that is in line with the principles of Binary Search. Stakeholder decisions and their allocated votes for each requirement are the basis for the approach, which yields computed values. The superiority of MVGB in terms of speed, fault tolerance, reliability, and other crucial criteria is revealed by a comparative analysis of the approach against alternative demand prioritizing methods, particularly Multi-voting and Binary Search.
Purnima Pal, Veena Grover, Manju Nandal, Saikat Gochhait, and Harsh Vikram Singh
IEEE
In recent years, heart disease has become a very serious threat to the health and safety of people all over the globe. Typically, this condition occurs when there is an insufficient supply of blood from the heart to various parts of the body, which hampers their usual operations. Early and timely detection of this disease holds paramount importance in preventing patients from further harm and saving their lives. Artificial intelligence (AI) has emerged as a pivotal tool in advancing heart disease prediction through its multifaceted roles. In this study, an intelligent computational model is introduced. This intelligent computational model encompasses multiple stages, including comprehensive data preprocessing and a strategic feature selection process utilizing correlation-based techniques. It also utilizes machine learning and deep neural networks to obtain a robust model for heart disease classification. Serval performance metrics are evaluated to observe the effectiveness of the proposed model. The proposed model achieved the highest classification accuracy of 99.01%. The proposed model contributes a powerful predictive model aimed at enhancing heart disease diagnosis, an imperative step toward effective patient care and potentially life-saving interventions.
Satya Reddy Satti, Jaswanth Singh Kumar Lankadasu, Ajay Sharma, Shamneesh Sharma, and Saikat Gochhait
IEEE
The global COVID-19 pandemic has resulted in significant loss of life and profoundly affected every aspect of human existence. A noteworthy area of study in this crisis is the use of deep learning (DL) models in medical imaging for the treatment of patients with COVID-19. This in-depth research delves into various methods of medical imaging, such as X-rays and computed tomography (CT) images, and their use in DL approaches for differentiating between COVID-19 and pneumonia. The paper outlines how DL techniques, including image localization, segmentation, registration, and classification, can aid in the detection of COVID-19. Recent evaluations have shown InstaCovNet-19 to have a remarkable classification accuracy of 99.80 percent when applied to an Xray dataset of 361 COVID-19 patients, 362 pneumonia patients, and 365 healthy individuals.
Angotu Saida, Nomula Mounika, Aleem Mohammad, E Shireesha, Saikat Gochhait, and Shrish Chogle
IEEE
Utilizing GSM and GPS, they pass on the messages to their family, relatives, or own people when a mishap is recognized or has happened. Suppose an individual is in a position where someone else is there to help, in that case, this venture assists with sending messages to their family members or companions and cautions the casualty's family or relatives. Accidents are on the ascent because of an increment in the number of vehicles out and about; we will most likely be unable to keep away from wounds, yet we can work salvage victims. Vehicle impact discovery is helpful in such circumstances. Mishap discovery utilizing shrewd GPS programming and GSM, phones, portable applications and vehicular impromptu organizations are among the frameworks recommended by different analysts. A vibration sensor distinguishes a crash utilizing piezoelectric impact, which is the measurement of specific products to produce an electric charge when these are under mechanical pressure. The casualty's close companions, family members, or rescue vehicle shows up at the mishap spot, which is followed by the GPS module.
Kishita Jain and Saikat Gochhait
IEEE
As the financial industry increasingly relies on digital infrastructures, the risk of cyberattacks on financial institutions is rapidly increasing. This paper analyzes the cyber threats and vulnerabilities in the Islamic finance sector and examines the role of risk management in mitigating these risks. The research paper emphasizes the importance of risk management in Islamic finance and presents a practical 5-step conceptual framework. This framework guides financial organizations in identifying potential risks, developing tailored controls, implementing mon-itoring systems and promoting continuous improvement.
Channi Sachdeva, Veer P Gangwar, Veena Grover, and Saikat Gochhait
IEEE
This research explores the complex terrain of cognitive dissonance that banking industry employees encounter while negotiating the changing environment of artificial intelligence (AI) integration. In a time when artificial intelligence (AI) is revolutionizing banking processes, it is critical to comprehend the elements that lead to cognitive dissonance among employees. The study takes a multimodal approach, integrating quantitative techniques to investigate the complex interactions between factors that affect cognitive dissonance in the workplace. Data were gathered from 344 bank employees via online questionnaires. The influence of AI -driven changes on job positions, workplace relationships, and employee expectations is examined in this study. It examines how individuals' flexibility, organizational culture changes, and communication gaps affect their capacity to align their views with the quickly changing technology world. The study also looks at the ethical implications of using AI in banking organizations, taking into account how moral issues might make employees feel more cognitively disoriented. The purpose of this study's findings is to offer useful information to human resource specialists, policymakers, and stakeholders in the banking sector who are attempting to manage the difficulties caused by cognitive dissonance while integrating AI. This research helps to design solutions that promote employee well-being, adaptation, and resilience in the face of revolutionary technology breakthroughs by elucidating the multiple dynamics at play. The purpose of this study is to investigate the many aspects of cognitive dissonance that affect employees in banking.
Abdul Bashiru Jibril, Frederick Pobee, Saikat Gochhait, and Ritesh Chugh
Informa UK Limited
Aakash Bhandary, Vruti Dobariya, Gokul Yenduri, Rutvij H. Jhaveri, Saikat Gochhait, and Francesco Benedetto
Institute of Electrical and Electronics Engineers (IEEE)
Effective energy management is crucial for sustainability, carbon reduction, resource conservation, and cost savings. However, conventional energy forecasting methods often lack accuracy, suggesting the need for advanced approaches. Artificial intelligence (AI) has emerged as a powerful tool for energy forecasting, but its lack of transparency and interpretability poses challenges for understanding its predictions. In response, Explainable AI (XAI) frameworks have been developed to enhance the transparency and interpretability of black-box AI models. Accordingly, this paper focuses on achieving accurate household energy consumption predictions by comparing prediction models based on several evaluation metrics, namely the Coefficient of Determination (R2), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE). The best model is identified by comparison after making predictions on unseen data, after which the predictions are explained by leveraging two XAI frameworks: Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP). These explanations help identify crucial characteristics contributing to energy consumption predictions, including insights into feature importance. Our findings underscore the significance of current consumption patterns and lagged energy consumption values in estimating energy usage. This paper further demonstrates the role of XAI in developing consistent and reliable predictive models.
Saikat Gochhait, Deepak K. Sharma, Rajkumar Singh Rathore, and Rutvij H. Jhaveri
Bentham Science Publishers Ltd.
Aim: Load forecasting with for efficient power system management Background:: Short-term energy load forecasting (STELF) is a valuable tool for utility companies and energy providers because it allows them to predict and plan for changes in energy. Method:: 1D CNN BI-LSTM model incorporating convolutional layers. Result:: The results provide the Root Mean Square Error of 0.952. The results shows that the proposed model outperforms the existing CNN based model with improved accuracy, hourly prediction, load forecasting. Conclusion:: The proposed model has several applications, including optimal energy allocation and demand-side management, which are essential for smart grid operation and control. The model’s ability to accurately management forecast electricity load will enable power utilities to optimize their generation.
Omar Alsayyed, Tareq Hamadneh, Hassan Al-Tarawneh, Mohammad Alqudah, Saikat Gochhait, Irina Leonova, Om Parkash Malik, and Mohammad Dehghani
MDPI AG
In this paper, a new bio-inspired metaheuristic algorithm called Giant Armadillo Optimization (GAO) is introduced, which imitates the natural behavior of giant armadillo in the wild. The fundamental inspiration in the design of GAO is derived from the hunting strategy of giant armadillos in moving towards prey positions and digging termite mounds. The theory of GAO is expressed and mathematically modeled in two phases: (i) exploration based on simulating the movement of giant armadillos towards termite mounds, and (ii) exploitation based on simulating giant armadillos’ digging skills in order to prey on and rip open termite mounds. The performance of GAO in handling optimization tasks is evaluated in order to solve the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that GAO is able to achieve effective solutions for optimization problems by benefiting from its high abilities in exploration, exploitation, and balancing them during the search process. The quality of the results obtained from GAO is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that GAO presents superior performance compared to competitor algorithms by providing better results for most of the benchmark functions. The statistical analysis of the Wilcoxon rank sum test confirms that GAO has a significant statistical superiority over competitor algorithms. The implementation of GAO on the CEC 2011 test suite and four engineering design problems show that the proposed approach has effective performance in dealing with real-world applications.
Soumya Pandey and Saikat Gochhait
AIP Publishing
Siddharth Mantraratnam, Saikat Gochhait, Ahmed J. Obaid, and A. H. Radie
AIP Publishing
Shalini Sinha, Saikat Gochhait, Ahmed J. Obaid, Azmi Shawkat Abdulbaqi, Watheq Naeem Alwan, Mohammed Ibrahim Mahdi, and Muthmainnah
AIP Publishing
Ambuj Mohan, Saikat Gochhait, Ahmed J. Obaid, Muthmainnah, and Miguel Cardoso
AIP Publishing
Selvakumar Thirumalaisamy, Kamaleshwar Thangavilou, Hariharan Rajadurai, Oumaima Saidani, Nazik Alturki, Sandeep kumar Mathivanan, Prabhu Jayagopal, and Saikat Gochhait
MDPI AG
Breast cancer is the second leading cause of mortality among women. Early and accurate detection plays a crucial role in lowering its mortality rate. Timely detection and classification of breast cancer enable the most effective treatment. Convolutional neural networks (CNNs) have significantly improved the accuracy of tumor detection and classification in medical imaging compared to traditional methods. This study proposes a comprehensive classification technique for identifying breast cancer, utilizing a synthesized CNN, an enhanced optimization algorithm, and transfer learning. The primary goal is to assist radiologists in rapidly identifying anomalies. To overcome inherent limitations, we modified the Ant Colony Optimization (ACO) technique with opposition-based learning (OBL). The Enhanced Ant Colony Optimization (EACO) methodology was then employed to determine the optimal hyperparameter values for the CNN architecture. Our proposed framework combines the Residual Network-101 (ResNet101) CNN architecture with the EACO algorithm, resulting in a new model dubbed EACO–ResNet101. Experimental analysis was conducted on the MIAS and DDSM (CBIS-DDSM) mammographic datasets. Compared to conventional methods, our proposed model achieved an impressive accuracy of 98.63%, sensitivity of 98.76%, and specificity of 98.89% on the CBIS-DDSM dataset. On the MIAS dataset, the proposed model achieved a classification accuracy of 99.15%, a sensitivity of 97.86%, and a specificity of 98.88%. These results demonstrate the superiority of the proposed EACO–ResNet101 over current methodologies.
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GRANT DETAILS
Department of Science and Industrial Research , Govt of India with Grant of Rs 13,000,00
Ministry of Foreign Affairs, Taiwan with Grant of Rs 12,000,00
University of Deusto, Spain with Research Grant of Rs 2,000,00
University of Extremadura, Spain with Research Grant of Rs 2,000,00
Samara State Medical University, Russia with Research Visit grant of Rs 2,500,00
Symbiosis International Deemed University with Travel and Research Grant of 4,000,000
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IFGL Refractories Ltd